Online Payment Fraud Detection Using Machine Learning

We utilize machine learning methods to identify online payment fraud that includes multifaceted techniques that involve data collection, feature selection, model training and continuous estimation. Here we give a high-level overview of how it will be designed:

Data Collection and Preparation

• Historical Transaction Data: Our work involves both fraudulent and genuine transactions by gathering the historical transaction data.
• Data Cleaning: To enhance the quality of the dataset, we eliminate outliers and handle missing values.
• Feature Engineering: Differentiating among fraudulent and genuine transactions, such as the time between transactions, transaction frequency and uncommon patterns in spending, we produce novel structures to help.

Feature Selection

• Analysis of Features: In our work we find the more important structures that contribute to fraud, by utilizing statistical measures.
• Dimensionality Reduction: We decrease the number of structures while remembering some essential information to apply methods like Principal Component Analysis (PCA).

Model Training

• Choosing Algorithms: Decision Trees, Random Forest, Support Vector Machines (SVM) or Neural Networks are the machine learning methods that we select similarly.
• Training and Validation: To train and validate the framework by utilizing an individual dataset to prevent overfitting by utilizing past data.
• Ensemble Methods: Our work enhances the forecasting accuracy by merging multiple frameworks.

Deployment and Real-time Analysis

• Integration: In our work, we identify the transaction in real time to deploy the framework into the payment processing pipeline.
• Threshold Setting: When a transaction is possible to be fraudulent, we define a threshold for the frameworks forecasting to decide.
• Real-Time Decision Making: For further review, we utilize our model to approve, decline or flag transactions.

Continuous Evaluation and Model Updating

• Feedback Loop: The findings of flagged transactions are utilized to further train and upgrade the framework by integrating the feedback model.
• Regular Retraining: To adjust alter patterns in fraud; we improve the framework frequently with novel data.

Challenges and Considerations

• Data Privacy: When handling user transaction data, we make sure to obedience with data security rules like GDPR.
• Model Explainability: For predictors, we make our framework’s decision interpretable that is important to give assurance and accountability.
• Adaptability: To novel and emerging kinds of frauds, we maintain the framework adaptive.

For fraud detection we utilize machine learning frameworks required to be extremely accurate to decrease false positives (genuine transactions flagged as fraud) and false negatives (fraudulent transactions not caught). To strike a balance among the sensitivity of the framework and the user experience, as too many false positives can lead to consumer dissatisfaction.

One could investigate into a particular machine learning method, data preprocessing approaches and framework combination methods, we give a more detailed explanation or to execute such a model.

Online Payment Fraud Detection Using Machine Learning Thesis Ideas

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1. Fraud Detection in Online Payments using Machine Learning Techniques

Keywords:

Fraud detection, Machine learning, Random Forest, Gradient Boosting algorithms, classification, Data pre-processing, Prediction

Our paper presents a feature-engineered ML-based model for detecting fraud transaction. When we process more data the method can change the method can improve experience and improve their performance. With the help of ML methods unfamiliar or individual data pattern will aid in finding any transaction that fraudulent are exposed. The XGBoost method is a cluster of DT that will used to get best result.

2. Online payment fraud: from anomaly detection to risk management

Keywords:

Payment fraud risk management, Anomaly detection, Ensemble models, Integration of machine learning and statistical risk modelling, Economic optimization machine learning outputs

There is an imbalanced data and complexity of fraud and classical ML can extended and decrease expected financial loss. So we use three methods to overcome the challenges: ML-based fraud detection, economic optimisation of ML results and a risk model to predict the fraud control measures. Our ML method decreases alone the predictable and unpredictable loss in three combined payment stations. Optimized ML model decreases the loss.

3. A Comparison Study of Fraud Detection in Usage of Credit Cards using Machine Learning

Keywords:

Machine Learning Fraud Detection, Credit Card Fraud, Online Fraud, Credit Card Scams

The goal of our paper is to design and generate a cutting-edge ML based fraud detection. We use some of the ML methods like XGBoost, SVM, DT, RF and LR. To enhance the efficiency of fraud detection we use a CNN method. Our suggested method overcomes the issue of credit card finding by joining the DL with ML methods.

4. An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms

Keywords:

Credit Card, Fraudulent Transaction, Confidentiality

We concentrate on recent credit card fraud practices and fraud detection methods executed in real time. We used various ML methods like Fuzzy-based SVM (FSVM), RF, LR and SVM. We utilize these methods for fraud transaction detection on the dataset gathered from credit card handlers utilized to classify genuine and fraud transaction. Our FSVM method gives the high performance accuracy.

5. Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques

Keywords:

Logistic Regression, Decision Tree, XGBoost, Artificial Neural Network, SMOTE, Under Sampling, Over Sampling

Our study establishes how the model uses multiple classifiers and data balance by utilize ML methods to learn about credit card fraud detection. We can detect the data as an imbalanced data that could gather not much optimal performance of model. The imbalanced data has done and observed by XGBoost that yield high performance. Random oversampling technique overcomes the imbalanced data. We used many data balancing method like o oversampling, under sampling, and SMOTE.

6. Credit Card Fraud Detection using Logistic Regression with Imbalanced Dataset

Keywords:

Test Data, Train Data

Our study examines the use of LR, a ML method to detect credit card fraud transaction in an imbalanced dataset. To overcome the problem of imbalanced data we work under sampling of majority class and oversampling of minority class. The outcome displays LR can give high accuracy to detect fraudulent transaction. We also highlight the possible of LR for credit card fraud detection with imbalance dataset.

7. Machine Learning based Data Mining for Detection of Credit Card Frauds

Keywords:                             

data mining techniques, financial institutions, Bayesian network, Clustering, Data cleaning, Neutral Network

We analyse various data mining methods to detect the credit card frauds. The procedure for data mining are described clearly that can be helpful for identifying fraudulent. Our study debates that Bayesian Network and DT are the effective method for data mining. We also offer the significance of RF method in the purpose of credit card fraud. Data cleaning and data visualization, are the ML process that permits to be improve with the support of data mining process of credit card.

8. Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks

 Keywords:

Graph neural network, graph centrality measure, medicare fraud detection

Our study validates the medicare fraud detection improved by presenting the graph analysis. We utilize open source dataset and then we combine them into a single dataset by changing them into a graph structure. We improve the medicare fraud detection model by utilizing two technique to reflect graph information i.e. GNN model and traditional ML method by utilizing graph centrality measures. GNN method gives the best performance.

9. Serverless Stream-Based Processing for Real Time Credit Card Fraud Detection Using Machine Learning

Keywords:

Adasyn, Feature Engineering, Real-time cloud deployment

Our paper uses different data imbalance handling methods liker Oversampling, Under Sampling, Synthetic Minority over Sampling and Adasyn and it effect the ML method for credit card fraud detection. For in-depth analysis we utilize four ML methods like LR, RF, XGBoost and DT have been selected for comparison. Each method can adjust for optimal hyperparameter before construct the model. Our XGBoost with oversampling offers the best result.

10. Fraud Detection in Banking Data by Machine Learning Techniques

Keywords:

Bayesian optimization, data Mining, deep learning, ensemble learning, hyper parameter, unbalanced data

Our paper considers the use of class weight-tuning hyperparameters to regulate the weight of fraud and genuine transaction. We also use Bayesian optimization to optimise the hyperparameters while considering the issue of imbalanced data. We suggested weight-tune as a pre-process for unbalanced data as well as CatBoost and XGBoost to enhance the performance of LightGBM method. CatBoost, LightGBM, and XGBoost are estimated separately by utilizing 5-fold cross validation method.